The present disclosure relates to computing systems, and, in particular, to performance management of computing systems.
Computer systems management data comprises fault, performance and utilization measurements. These data are commonly visualized as time-series style charts with multiple data sets plotted against time on the x-axis. A typical such polyline chart might display either a single systems management metric with a separate data set for each different resource/object, or a single object with a separate data set for each different systems management metric.
A time-series chart typically displays consecutive time intervals that show the transition of measurements from one point in time of interest to another. Each transitional time interval along the way is displayed with many data points. A time-series chart may then be combined with more than one y-axis scale and with optional time interval instrumentation. Together, combining all this information in this manner may deliver a lot of visual noise, which may obscure the presence of significant patterns of system activity.
The visual noise difficulty may be exacerbated by large numbers of resources or objects present in commercial-scale enterprise networks. Because of the time and logistical challenges with manually comparing large numbers of time-series charts, detailed performance or workload analysis reporting may be restricted to the key system objects only. Because an enterprise may depend on the performance analysis to identify what the key system objects are, they may be reduced to guessing which of perhaps tens of thousands of objects deserve special monitoring. The current volatility of enterprise systems and networks with the introduction of cloud and Software as a Service (SaaS) architectures may make system usage even more unpredictable.
For enterprise IT business requirements, such as application performance management, infrastructure management, and service delivery management, companies may need the ability to quickly isolate who the major system resource users are to diagnose and response to user problems and service level breaches in a timely manner. This may require retrieving and sorting appropriate metrics for all existing objects of a type, and displaying only a “Top N” by a suitable measure. Time-series charts with multiple data sets on a line chart may not be a practical way to display comparative “Top N” data. “Top N” data is generally presented in non-graphical tables or low quality ordered bar charts. A “Top N” bar chart may be restricted to displaying a single metric, which may require several “Top N” bar charts to be manually visually compared.
The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.